DL-TF-TORCH-CH1-Basic.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1ceGafkxSD9115GeKvJZBZK7V2hSNsd5o","timestamp":1695436181510}],"toc_visible":true,"authorship_tag":"ABX9TyPARAn9BNgEM2szSbzELgrq"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["<table align=\"left\">\n","  <td>\n","    <a target=\"_blank\" href=\"https://colab.research.google.com/github/hccho2/DL-UPMTP/blob/master/DL-TF-TORCH-CH1-Basic.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n","  </td>\n","</table>"],"metadata":{"id":"KygmS4ibPltH"}},{"cell_type":"code","source":["import tensorflow as tf\n","import torch\n","import numpy as np\n","import librosa\n","import torchaudio\n","\n","print(f'tensorflow: {tf.__version__}')\n","print(f'torch: {torch.__version__}')\n","print(f'numpy: {np.__version__}')\n","print(f'librosa: {librosa.__version__}')\n","print(f'torchaudio: {torchaudio.__version__}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5XE2A2cL-p6H","executionInfo":{"status":"ok","timestamp":1695339595699,"user_tz":-540,"elapsed":8571,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"14a27faf-f4ac-4413-b229-2ff3efbbe776"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tensorflow: 2.13.0\n","torch: 2.0.1+cu118\n","numpy: 1.23.5\n","librosa: 0.10.1\n","torchaudio: 2.0.2+cu118\n"]}]},{"cell_type":"markdown","source":["# 1.1 Numpy Array"],"metadata":{"id":"NMN1M4sZe_50"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"qKIyN0mRe7fP"},"outputs":[],"source":["import numpy as np"]},{"cell_type":"code","source":["x = np.random.randn(2,3)\n","y = np.zeros_like(x)\n","z = np.ones_like(x)\n","print(f'x: {x}\\n\\ny: {y}\\n\\nz: {z}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TTZDXOKte9CD","executionInfo":{"status":"ok","timestamp":1695339747019,"user_tz":-540,"elapsed":343,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"386d1bf4-c9a6-4947-8290-76fec20a4c5b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["x: [[ 1.72574131 -0.04310181  0.07090093]\n"," [ 0.27845374  0.83066721 -0.92458249]]\n","\n","y: [[0. 0. 0.]\n"," [0. 0. 0.]]\n","\n","z: [[1. 1. 1.]\n"," [1. 1. 1.]]\n"]}]},{"cell_type":"markdown","source":["## 1.1.2 np.printoptions\n","- suppress=True이면 부동 소수점 형식으로 출력한다"],"metadata":{"id":"Kup0INILfZ9y"}},{"cell_type":"code","source":["X = np.random.randn(3,4)*0.0001\n","print('scientific notation:')\n","print(X)\n","with np.printoptions(precision=6, suppress=True):\n","    print('6 decimal point:')\n","    print(X)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XHhuvUMifZbW","executionInfo":{"status":"ok","timestamp":1695339971554,"user_tz":-540,"elapsed":264,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"1c28eeb4-d09c-4997-e634-e1040ff3a8a4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["scientific notation:\n","[[ 2.08946728e-05  4.68413860e-05  3.67476524e-05 -1.14428545e-04]\n"," [-6.80781585e-05 -1.29094780e-04 -1.18639217e-04  8.20218896e-05]\n"," [ 1.47101035e-05  1.15003281e-04 -2.40582507e-05  1.63284545e-04]]\n","6 decimal point:\n","[[ 0.000021  0.000047  0.000037 -0.000114]\n"," [-0.000068 -0.000129 -0.000119  0.000082]\n"," [ 0.000015  0.000115 -0.000024  0.000163]]\n"]}]},{"cell_type":"markdown","source":["## 1.1.3 basic indexing & advanced indexing\n","- basic indexing은 view를 만들기 때문에 원본 데이터를 변화시킬 수 있다.\n","- advanced indexing은 값을 복사해 온다."],"metadata":{"id":"2wmPOF9QgYDC"}},{"cell_type":"code","source":["X = np.array([[0.5267, 0.0362, 0.171,  0.2661],\n","                [0.0076,  0.0247, 0.8906, 0.0771],\n","                [0.265,   0.2017, 0.4556, 0.0777],\n","                [0.0972,  0.4766, 0.3375, 0.0887],\n","                [0.11,    0.2512, 0.2637, 0.3751]])\n","X[1:4,1:3]  # basic indexing"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SKdb8doUSVbj","executionInfo":{"status":"ok","timestamp":1695340606407,"user_tz":-540,"elapsed":268,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"3e434954-cb39-49b6-eed9-d12bc40fa9dd"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.0247, 0.8906],\n","       [0.2017, 0.4556],\n","       [0.4766, 0.3375]])"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["X[X>0.5] # advanced indexing"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hIMledqkWrRU","executionInfo":{"status":"ok","timestamp":1695340607025,"user_tz":-540,"elapsed":4,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"9b5673c4-e72c-48f7-92bf-3d8f1446810b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.5267, 0.8906])"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["x = np.array([[ -5,   2,  0, -7],\n","              [ -1,   9,  3,  8],\n","              [ -3,  -3,  4,  6]])\n","\n","\n","a = x[::2, 1]  # view\n","b = x[[0,2],[1,1]]  # copy\n","a[0] = -99 # x의 값도 변한다.\n","b[1] = -99 # x와 무관\n","\n","print(x)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d12m_rxBfHWi","executionInfo":{"status":"ok","timestamp":1674609577994,"user_tz":-540,"elapsed":5,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"498ebb5d-ee3e-427d-83c2-922d232f6670"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[ -5 -99   0  -7]\n"," [ -1   9   3   8]\n"," [ -3  -3   4   6]]\n"]}]},{"cell_type":"markdown","source":["np.argmax()로 최댓값을 가지는 index 추출"],"metadata":{"id":"m2V4tY7BMCCb"}},{"cell_type":"code","source":["X = np.array([[0.5267, 0.0362, 0.171,  0.2661],\n","                [0.0076,  0.0247, 0.8906, 0.0771],\n","                [0.265,   0.2017, 0.4556, 0.0777],\n","                [0.0972,  0.4766, 0.3375, 0.0887],\n","                [0.11,    0.2512, 0.2637, 0.3751]])\n","X[np.arange(len(X)),X.argmax(axis=-1)]  # 각 행에서 최대값만 추출"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SykbmXMwgkvT","executionInfo":{"status":"ok","timestamp":1695341069661,"user_tz":-540,"elapsed":258,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"4e93cb31-bd12-4af7-c4b9-6c0c6d2ee964"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.5267, 0.8906, 0.4556, 0.4766, 0.3751])"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["X[np.arange(X.shape[0]),X.argmax(axis=-1)]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e3J0AV90m-Z0","executionInfo":{"status":"ok","timestamp":1674626484539,"user_tz":-540,"elapsed":8,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"5794bb64-9556-4a2c-f792-cb7ddd4e4f98"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.5267, 0.8906, 0.4556, 0.4766, 0.3751])"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["a = np.array([0, 0, 0, 0, 0, 0])\n","idx = [0,1,1,3]\n","b = np.array([1,2,10,20])\n","np.add.at(a, idx, b)\n","print(a)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sGDyUDuZn_tE","executionInfo":{"status":"ok","timestamp":1674630325686,"user_tz":-540,"elapsed":9,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"b679eae1-62a6-4cb8-ad3e-f530b211649d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[ 1 12  0 20  0  0]\n"]}]},{"cell_type":"markdown","source":["## 1.1.4 np.add.at\n","\n","![image.png](data:image/png;base64,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)"],"metadata":{"id":"bYow247tp7pH"}},{"cell_type":"code","source":["a = np.array([0, 0, 0, 0, 0, 0])\n","idx = [0,1,1,3]\n","b = np.array([1,2,10,20])\n","np.add.at(a,idx,b)\n","a  # [ 1, 10,  0, 20,  0,  0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FqIj9HbNp_7_","executionInfo":{"status":"ok","timestamp":1695341849482,"user_tz":-540,"elapsed":271,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"c742eded-e1f4-4535-8d30-6fb1dd6d0334"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 1, 12,  0, 20,  0,  0])"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","source":["# np.add.at과 비교\n","a = np.array([0, 0, 0, 0, 0, 0])\n","idx = [0,1,1,3]\n","b = np.array([1,2,10,20])\n","a[idx] += b\n","a  # [ 1, 10,  0, 20,  0,  0]  <--- 중복 인덱스에 한번만 더해준다."],"metadata":{"id":"OMznyamuPmmr"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["2-dim array\n","- ([1,0],[1,3])  --> a[1,1], a[0,3]에 값을 더해 준다."],"metadata":{"id":"KmayLT8Kvyyo"}},{"cell_type":"code","source":["a = np.zeros([2,4],int)\n","np.add.at(a,([1,0],[1,3]),[5,10])\n","print(a)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JhOP_NigqJsv","executionInfo":{"status":"ok","timestamp":1695341607669,"user_tz":-540,"elapsed":297,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"dfb88256-5fe6-40b2-dfad-f1bdfcf9fd3f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 0  0  0 10]\n"," [ 0  5  0  0]]\n"]}]},{"cell_type":"markdown","source":["# 1.2 Tensorflow 기초"],"metadata":{"id":"9MBN-REWSFrA"}},{"cell_type":"code","source":["import tensorflow as tf  # 2.9.2\n","import numpy as np\n","\n","tf.__version__"],"metadata":{"id":"uiExqLAJSNwI","executionInfo":{"status":"ok","timestamp":1674880080928,"user_tz":-540,"elapsed":433,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"16aa8f97-1449-44a9-8a59-dfcfcc89e947"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'2.9.2'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","source":["## 1.2.1 tf.constant, tf.convert_to_tensor, tf.Variable"],"metadata":{"id":"Suhf6rrCSP54"}},{"cell_type":"code","source":["a = np.array([[ 0.84, -0.15, -1.33],\n","              [ 0.11, -0.36,  0.08]])\n","X = tf.convert_to_tensor(a) # symbolic tensor를 넘길 수도 있다.\n","\n","Y = tf.constant(a,shape=(6,)) # reshape도 가능.\n","\n","V = tf.Variable(a) # V.trainable = True\n","V.assign_add(X)\n","\n","Z = tf.convert_to_tensor(V) # symbolic tensor를 넘길 수도 있다.\n","\n","print(f'X: {X}')\n","print(f'Y: {Y}')\n","print(f'V: {V}')\n","print(f'Z: {Z}')"],"metadata":{"id":"jbZsZEIFSTbD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1695342353150,"user_tz":-540,"elapsed":265,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"cd2bb61b-30a8-4c38-b1e8-9d31cf806cb7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["X: [[ 0.84 -0.15 -1.33]\n"," [ 0.11 -0.36  0.08]]\n","Y: [ 0.84 -0.15 -1.33  0.11 -0.36  0.08]\n","V: <tf.Variable 'Variable:0' shape=(2, 3) dtype=float64, numpy=\n","array([[ 1.68, -0.3 , -2.66],\n","       [ 0.22, -0.72,  0.16]])>\n","Z: [[ 1.68 -0.3  -2.66]\n"," [ 0.22 -0.72  0.16]]\n"]}]},{"cell_type":"code","source":["type(V), type(V+X)"],"metadata":{"id":"XTi4Vs9NSTXJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1695342615986,"user_tz":-540,"elapsed":6,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"e1e36cf4-4b39-4192-fd61-c57624adfa7f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(tensorflow.python.ops.resource_variable_ops.ResourceVariable,\n"," tensorflow.python.framework.ops.EagerTensor)"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","source":["## 1.2.2 tf.gather, tf.gather_nd"],"metadata":{"id":"G6nnbUqkSHf4"}},{"cell_type":"code","source":["x = np.arange(12).reshape(4,3)\n","print(f'x: {x}\\n')\n","print(tf.gather(x, indices=[3,0,2],axis=0))  # 행 추출\n","print(tf.gather(x, indices=[2,1], axis=1)) # 열 추출"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtJXHZ2r9GK1","executionInfo":{"status":"ok","timestamp":1695342934728,"user_tz":-540,"elapsed":284,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"614b6e9a-4cf4-4c41-c605-ad19090f0d40"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["x: [[ 0  1  2]\n"," [ 3  4  5]\n"," [ 6  7  8]\n"," [ 9 10 11]]\n","\n","tf.Tensor(\n","[[ 9 10 11]\n"," [ 0  1  2]\n"," [ 6  7  8]], shape=(3, 3), dtype=int64)\n","tf.Tensor(\n","[[ 2  1]\n"," [ 5  4]\n"," [ 8  7]\n"," [11 10]], shape=(4, 2), dtype=int64)\n"]}]},{"cell_type":"code","source":["tf.gather(x,[2,2,0,1],axis=1) # tf.gather(x,idx,axis=1,batch_dims=0)과 동일"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bhrpCp296KVu","executionInfo":{"status":"ok","timestamp":1695343141027,"user_tz":-540,"elapsed":268,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"d7acf9a3-c374-49b9-8921-e1836fd46191"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(4, 4), dtype=int64, numpy=\n","array([[ 2,  2,  0,  1],\n","       [ 5,  5,  3,  4],\n","       [ 8,  8,  6,  7],\n","       [11, 11,  9, 10]])>"]},"metadata":{},"execution_count":38}]},{"cell_type":"code","source":["X = np.array([[0.5267, 0.0362, 0.171,  0.2661],\n","                [0.0076,  0.0247, 0.8906, 0.0771],\n","                [0.265,   0.2017, 0.4556, 0.0777],\n","                [0.0972,  0.4766, 0.3375, 0.0887],\n","                [0.11,    0.2512, 0.2637, 0.3751]])\n","print('X: \\n', x)\n","idx = np.array([0,2,2,1,3])\n","print('idx: ', idx)\n","\n","print('='*20)\n","A = X[np.arange(len(X)),idx ]\n","print(\"Numpy: \", A)\n","\n","assert len(idx) ==len(X)\n","B  = tf.gather(X,idx,axis=1,batch_dims=1) # [(0,0), (1,2), (2,2), (3,1), (4,3)] 원소를 추출한다.\n","print(\"TF(batch_dims=1): \", B)\n","\n","idx2 = [0,2,2,1,3,1,2]\n","C  = tf.gather(X,idx2,axis=1,batch_dims=0)\n","print(\"TF(batch_dims=0): \", C)"],"metadata":{"id":"20I14A_bqKXN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1695343690910,"user_tz":-540,"elapsed":288,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"97b2ca04-ec3b-4f00-ed7a-a880818123bc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["X: \n"," [[ 0  1  2]\n"," [ 3  4  5]\n"," [ 6  7  8]\n"," [ 9 10 11]]\n","idx:  [0 2 2 1 3]\n","====================\n","Numpy:  [0.5267 0.8906 0.4556 0.4766 0.3751]\n","TF(batch_dims=1):  tf.Tensor([0.5267 0.8906 0.4556 0.4766 0.3751], shape=(5,), dtype=float64)\n","TF(batch_dims=0):  tf.Tensor(\n","[[0.5267 0.171  0.171  0.0362 0.2661 0.0362 0.171 ]\n"," [0.0076 0.8906 0.8906 0.0247 0.0771 0.0247 0.8906]\n"," [0.265  0.4556 0.4556 0.2017 0.0777 0.2017 0.4556]\n"," [0.0972 0.3375 0.3375 0.4766 0.0887 0.4766 0.3375]\n"," [0.11   0.2637 0.2637 0.2512 0.3751 0.2512 0.2637]], shape=(5, 7), dtype=float64)\n"]}]},{"cell_type":"markdown","source":["tf.gather_nd"],"metadata":{"id":"MfT8Y3cRsia_"}},{"cell_type":"code","source":["idx_ = list(zip(np.arange(len(idx)),idx)) # [(0, 0), (1, 2), (2, 2), (3, 1), (4, 3)]\n","print('idx: ', idx_)\n","tf.gather_nd(X,idx_)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0gBqbZcA9Ft8","executionInfo":{"status":"ok","timestamp":1674797478817,"user_tz":-540,"elapsed":450,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"48f332b7-9980-4468-ffc1-29beefe9b0e6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["idx:  [(0, 0), (1, 2), (2, 2), (3, 1), (4, 3)]\n"]},{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(5,), dtype=float64, numpy=array([0.5267, 0.8906, 0.4556, 0.4766, 0.3751])>"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","source":["![image.png](data:image/png;base64,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)"],"metadata":{"id":"8f3pqQj8YIsp"}},{"cell_type":"code","source":["A = np.arange(20).reshape(5,4)\n","print(A)\n","tf.gather_nd(A,[[2,1],[3,3],[4,0],[0,2]])\n","\n"],"metadata":{"id":"oTXJTjxQAYM1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1674833808326,"user_tz":-540,"elapsed":404,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"4b11ae12-0bff-46ac-f3b7-605ca776d13e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 0  1  2  3]\n"," [ 4  5  6  7]\n"," [ 8  9 10 11]\n"," [12 13 14 15]\n"," [16 17 18 19]]\n"]},{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(4,), dtype=int64, numpy=array([ 9, 15, 16,  2])>"]},"metadata":{},"execution_count":3}]},{"cell_type":"markdown","source":["## 1.2.4 Function vs Class API"],"metadata":{"id":"pmEHufJmWoJW"}},{"cell_type":"code","source":["x = np.array([[-0.42451359, -0.5520304 , -0.39618764],\n","              [ 0.81954556,  0.07631097, -0.45242647]])"],"metadata":{"id":"3HVsJ7G8AaoN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tf.nn.relu(x)"],"metadata":{"id":"PcQM9ESjF3EY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1695344704022,"user_tz":-540,"elapsed":5,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"2ccdf326-4b93-491c-9512-193a5604aa76"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(2, 3), dtype=float64, numpy=\n","array([[0.        , 0.        , 0.        ],\n","       [0.81954556, 0.07631097, 0.        ]])>"]},"metadata":{},"execution_count":42}]},{"cell_type":"code","source":["L = tf.keras.layers.ReLU()\n","L(x)"],"metadata":{"id":"jC8mG3b3GMGn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1695344704482,"user_tz":-540,"elapsed":4,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"c21b09e5-dce1-41e9-fc6d-a2f7bdbb3118"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n","array([[0.        , 0.        , 0.        ],\n","       [0.81954557, 0.07631097, 0.        ]], dtype=float32)>"]},"metadata":{},"execution_count":43}]},{"cell_type":"markdown","source":["# 1.3 Pytorch 기초"],"metadata":{"id":"dPeeCYxvoTBN"}},{"cell_type":"code","source":["import torch\n","import numpy as np\n","torch.__version__  # 1.13.1+cu116"],"metadata":{"id":"4LZJgbcMHI1w","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1695344707666,"user_tz":-540,"elapsed":6,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"a049e6a1-34f5-4142-d860-7d12da3980a6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'2.0.1+cu118'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":44}]},{"cell_type":"code","source":["torch.tensor([1,2]).dtype, torch.tensor([1.,2.]).dtype, torch.tensor(np.array([1.,2.])).dtype, torch.tensor(np.array([1.,2.]),dtype=torch.float).dtype"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bLZZXoOdoYhJ","executionInfo":{"status":"ok","timestamp":1695344733812,"user_tz":-540,"elapsed":276,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"d02981e8-5ec3-4c81-d074-7d1188f41d8c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.int64, torch.float32, torch.float64, torch.float32)"]},"metadata":{},"execution_count":45}]},{"cell_type":"code","source":["torch.FloatTensor([1,2]).dtype, torch.FloatTensor([1.,2.]).dtype, torch.FloatTensor(np.array([1.,2.])).dtype, torch.FloatTensor(np.array([1.,2.])).dtype"],"metadata":{"id":"bK8dL2HLonOB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1674884493460,"user_tz":-540,"elapsed":302,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"4981446b-f56b-47e7-fbd3-5123e40540d8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.float32, torch.float32, torch.float32, torch.float32)"]},"metadata":{},"execution_count":23}]},{"cell_type":"markdown","source":["device"],"metadata":{"id":"LtrHoRnq5Zzl"}},{"cell_type":"code","source":["x = torch.randn(2,3,device='cpu')\n","if torch.cuda.is_available():\n","    y = x.to('cuda')\n","    print(x)\n","    print(y)"],"metadata":{"id":"FGHPMupYozfp"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 1.3.2 Numpy Tensorflow vs Pytorch\n","- Numpy와 Pytorch의 method 이름은 동일한 것이 많다."],"metadata":{"id":"2MUuQXQ4gqMI"}},{"cell_type":"code","source":["import numpy as np\n","import tensorflow as tf\n","import torch"],"metadata":{"id":"rVcAqez9hCMb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["a = np.array([[ 1.85, -1.33, -0.95, -0.40, -0.58],\n","              [-0.17, -0.15,  0.52,  0.72 , 0.27]])\n","b = np.array([[ 1.38,  0.88,  0.72],\n","              [ 1.88, -0.18 , -0.57],\n","              [-0.28, -0.80,  0.22],\n","              [-1.78, -0.66, -2.46],\n","              [-0.86, -0.22, -1.87]])\n","\n","a_ = torch.tensor(a)\n","b_ = torch.tensor(b)"],"metadata":{"id":"CVed9w4P5tSW"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["sum"],"metadata":{"id":"arvvGgjblXDX"}},{"cell_type":"code","source":["print(a.sum()) # np.sum(a)\n","print(tf.reduce_sum(a)) # Tensorflow Tensor로 명시적으로 변경하지 않아도 된다.\n","print(a_.sum()) # torch.sum(a_)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZK3rsgFUkQ6P","executionInfo":{"status":"ok","timestamp":1695345007119,"user_tz":-540,"elapsed":10,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"7dfb877b-f832-4eea-e3e3-7cb3079fbde8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["-0.21999999999999997\n","tf.Tensor(-0.21999999999999975, shape=(), dtype=float64)\n","tensor(-0.2200, dtype=torch.float64)\n"]}]},{"cell_type":"markdown","source":["mean"],"metadata":{"id":"RaZzBusalaEv"}},{"cell_type":"code","source":["print(a.mean()) # np.mean(a)\n","print(tf.reduce_mean(a)) # Tensorflow Tensor로 명시적으로 변경하지 않아도 된다.\n","print(a_.mean()) # torch.mean(a_)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AaJoSny4lbsG","executionInfo":{"status":"ok","timestamp":1695345010111,"user_tz":-540,"elapsed":300,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"a5bfd7c5-4e1f-4b7a-fbdf-d90bb772ff15"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["-0.022\n","tf.Tensor(-0.021999999999999974, shape=(), dtype=float64)\n","tensor(-0.0220, dtype=torch.float64)\n"]}]},{"cell_type":"markdown","source":["max"],"metadata":{"id":"HJYHHo_-lcMh"}},{"cell_type":"code","source":["print(a.max(axis=-1)) # np.max(a)\n","print(tf.reduce_max(a,axis=-1)) # Tensorflow Tensor로 명시적으로 변경하지 않아도 된다.\n","print(a_.max(axis=-1)) # torch.max(a_)\n","print('===')\n","print(a_.max()) # torch.max(a_,axis=-1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cRoAHvUPlcyq","executionInfo":{"status":"ok","timestamp":1695345012544,"user_tz":-540,"elapsed":286,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"2dfe7ec0-18bb-40e0-c7b1-b379cdeca401"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[1.85 0.72]\n","tf.Tensor([1.85 0.72], shape=(2,), dtype=float64)\n","torch.return_types.max(\n","values=tensor([1.8500, 0.7200], dtype=torch.float64),\n","indices=tensor([0, 3]))\n","===\n","tensor(1.8500, dtype=torch.float64)\n"]}]},{"cell_type":"markdown","source":["argmax"],"metadata":{"id":"m3RIqflvlVBd"}},{"cell_type":"code","source":["print(a.argmax(axis=-1))\n","print(tf.argmax(a,axis=-1))\n","print(a_.argmax(axis=-1))  # torch.argmax(a_,axis=-1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dsmh09KzkRlS","executionInfo":{"status":"ok","timestamp":1695345015364,"user_tz":-540,"elapsed":266,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"e1c84757-eec7-439c-c661-fbf91c906282"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[0 3]\n","tf.Tensor([0 3], shape=(2,), dtype=int64)\n","tensor([0, 3])\n"]}]},{"cell_type":"code","source":["print(np.matmul(a,b))\n","print(tf.matmul(a,b))\n","print(torch.matmul(a_,b_))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MmxC8p3NhPRu","executionInfo":{"status":"ok","timestamp":1695345027835,"user_tz":-540,"elapsed":273,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"8302c367-0198-47fe-e974-5b0d30b43bbd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 1.5294  3.019   3.9497]\n"," [-2.176  -1.0732 -2.1986]]\n","tf.Tensor(\n","[[ 1.5294  3.019   3.9497]\n"," [-2.176  -1.0732 -2.1986]], shape=(2, 3), dtype=float64)\n","tensor([[ 1.5294,  3.0190,  3.9497],\n","        [-2.1760, -1.0732, -2.1986]], dtype=torch.float64)\n"]}]},{"cell_type":"code","source":["a_.max(axis=-1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uw4VWpIHi85b","executionInfo":{"status":"ok","timestamp":1695345034330,"user_tz":-540,"elapsed":267,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"c9e8e21a-e724-488f-c39b-aedd2e99d440"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.return_types.max(\n","values=tensor([1.8500, 0.7200], dtype=torch.float64),\n","indices=tensor([0, 3]))"]},"metadata":{},"execution_count":53}]},{"cell_type":"markdown","source":["# 1.4 Pandas\n","\n","- https://www.kaggle.com/competitions/titanic/data --> train.csv, test.csv 파일을 가져오자\n","- 책 1.5.1 참조"],"metadata":{"id":"4hAfXWKDi2Cb"}},{"cell_type":"markdown","source":["## 1.4.1 Tatanic 데이터셋"],"metadata":{"id":"qimsK0FUdqDw"}},{"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import numpy as np"],"metadata":{"id":"Zs7n5tvFmD4l"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_df = pd.read_csv('train.csv')\n","test_df = pd.read_csv('test.csv')"],"metadata":{"id":"dvRhin1Aj63j"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_df.head(10)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"63xH912bmK5Q","executionInfo":{"status":"ok","timestamp":1695345408371,"user_tz":-540,"elapsed":293,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"2c7defcb-6c5f-4e1f-c817-5817d284be41"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   PassengerId  Survived  Pclass  \\\n","0            1         0       3   \n","1            2         1       1   \n","2            3         1       3   \n","3            4         1       1   \n","4            5         0       3   \n","5            6         0       3   \n","6            7         0       1   \n","7            8         0       3   \n","8            9         1       3   \n","9           10         1       2   \n","\n","                                                Name     Sex   Age  SibSp  \\\n","0                            Braund, Mr. Owen Harris    male  22.0      1   \n","1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n","2                             Heikkinen, Miss. Laina  female  26.0      0   \n","3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n","4                           Allen, Mr. William Henry    male  35.0      0   \n","5                                   Moran, Mr. James    male   NaN      0   \n","6                            McCarthy, Mr. Timothy J    male  54.0      0   \n","7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n","8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n","9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n","\n","   Parch            Ticket     Fare Cabin Embarked  \n","0      0         A/5 21171   7.2500   NaN        S  \n","1      0          PC 17599  71.2833   C85        C  \n","2      0  STON/O2. 3101282   7.9250   NaN        S  \n","3      0            113803  53.1000  C123        S  \n","4      0            373450   8.0500   NaN        S  \n","5      0            330877   8.4583   NaN        Q  \n","6      0             17463  51.8625   E46        S  \n","7      1            349909  21.0750   NaN        S  \n","8      2            347742  11.1333   NaN        S  \n","9      0            237736  30.0708   NaN        C  "],"text/html":["\n","  <div id=\"df-9d86a65a-55a4-456d-87ae-c7d6a431b253\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Braund, Mr. Owen Harris</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>Heikkinen, Miss. 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":58}]},{"cell_type":"code","source":["train_df.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BcUzSW8lm9PD","executionInfo":{"status":"ok","timestamp":1695345739254,"user_tz":-540,"elapsed":287,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"85e77bb2-bf16-4e29-fcf0-9fd00e568110"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 12 columns):\n"," #   Column       Non-Null Count  Dtype  \n","---  ------       --------------  -----  \n"," 0   PassengerId  891 non-null    int64  \n"," 1   Survived     891 non-null    int64  \n"," 2   Pclass       891 non-null    int64  \n"," 3   Name         891 non-null    object \n"," 4   Sex          891 non-null    object \n"," 5   Age          714 non-null    float64\n"," 6   SibSp        891 non-null    int64  \n"," 7   Parch        891 non-null    int64  \n"," 8   Ticket       891 non-null    object \n"," 9   Fare         891 non-null    float64\n"," 10  Cabin        204 non-null    object \n"," 11  Embarked     889 non-null    object \n","dtypes: float64(2), int64(5), object(5)\n","memory usage: 83.7+ KB\n"]}]},{"cell_type":"code","source":["test_df.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JFUk6DiHB1zd","executionInfo":{"status":"ok","timestamp":1695345743053,"user_tz":-540,"elapsed":265,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"aa2e7c41-222c-4813-ebf9-2339c29d9269"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 418 entries, 0 to 417\n","Data columns (total 11 columns):\n"," #   Column       Non-Null Count  Dtype  \n","---  ------       --------------  -----  \n"," 0   PassengerId  418 non-null    int64  \n"," 1   Pclass       418 non-null    int64  \n"," 2   Name         418 non-null    object \n"," 3   Sex          418 non-null    object \n"," 4   Age          332 non-null    float64\n"," 5   SibSp        418 non-null    int64  \n"," 6   Parch        418 non-null    int64  \n"," 7   Ticket       418 non-null    object \n"," 8   Fare         417 non-null    float64\n"," 9   Cabin        91 non-null     object \n"," 10  Embarked     418 non-null    object \n","dtypes: float64(2), int64(4), object(5)\n","memory usage: 36.0+ KB\n"]}]},{"cell_type":"code","source":["# missing data 확인\n","test_df[test_df['Fare'].isnull()==True]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"uSyQDw72FKDd","executionInfo":{"status":"ok","timestamp":1695345748075,"user_tz":-540,"elapsed":308,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"00fc51f5-0f0f-4be9-9628-f7a70def92fa"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     PassengerId  Pclass                Name   Sex   Age  SibSp  Parch Ticket  \\\n","152         1044       3  Storey, Mr. Thomas  male  60.5      0      0   3701   \n","\n","     Fare Cabin Embarked  \n","152   NaN   NaN        S  "],"text/html":["\n","  <div id=\"df-5ad323cb-55db-470d-a168-bc78299784f0\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>152</th>\n","      <td>1044</td>\n","      <td>3</td>\n","      <td>Storey, Mr. Thomas</td>\n","      <td>male</td>\n","      <td>60.5</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3701</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ad323cb-55db-470d-a168-bc78299784f0')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-5ad323cb-55db-470d-a168-bc78299784f0 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":61}]},{"cell_type":"code","source":["# 수치 데이터에 대한 요약 정보\n","train_df.describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"ZA3LBikzmMTH","executionInfo":{"status":"ok","timestamp":1695345804936,"user_tz":-540,"elapsed":13,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"87c60548-75b5-4791-f116-9f8163af8f6c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       PassengerId    Survived      Pclass         Age       SibSp  \\\n","count   891.000000  891.000000  891.000000  714.000000  891.000000   \n","mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n","std     257.353842    0.486592    0.836071   14.526497    1.102743   \n","min       1.000000    0.000000    1.000000    0.420000    0.000000   \n","25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n","50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n","75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n","max     891.000000    1.000000    3.000000   80.000000    8.000000   \n","\n","            Parch        Fare  \n","count  891.000000  891.000000  \n","mean     0.381594   32.204208  \n","std      0.806057   49.693429  \n","min      0.000000    0.000000  \n","25%      0.000000    7.910400  \n","50%      0.000000   14.454200  \n","75%      0.000000   31.000000  \n","max      6.000000  512.329200  "],"text/html":["\n","  <div id=\"df-570494f6-1715-4da4-a093-278b9a24fe9d\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>714.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>446.000000</td>\n","      <td>0.383838</td>\n","      <td>2.308642</td>\n","      <td>29.699118</td>\n","      <td>0.523008</td>\n","      <td>0.381594</td>\n","      <td>32.204208</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>257.353842</td>\n","      <td>0.486592</td>\n","      <td>0.836071</td>\n","      <td>14.526497</td>\n","      <td>1.102743</td>\n","      <td>0.806057</td>\n","      <td>49.693429</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.420000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>223.500000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>20.125000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>7.910400</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>446.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>28.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>14.454200</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>668.500000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>38.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>31.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>891.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>80.000000</td>\n","      <td>8.000000</td>\n","      <td>6.000000</td>\n","      <td>512.329200</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":64}]},{"cell_type":"code","source":["train_df[['Sex','Survived']].value_counts()  # Series"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qc5GAZSFrQca","executionInfo":{"status":"ok","timestamp":1695346075423,"user_tz":-540,"elapsed":316,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"1a091242-9fec-483c-a873-f83eb304fee7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Sex     Survived\n","male    0           468\n","female  1           233\n","male    1           109\n","female  0            81\n","dtype: int64"]},"metadata":{},"execution_count":65}]},{"cell_type":"markdown","source":["Code A"],"metadata":{"id":"kBc81Cm3gj9Y"}},{"cell_type":"code","source":["f,ax=plt.subplots(1,2,figsize=(12,5))\n","train_df[['Sex','Survived']].groupby(['Sex']).count().plot.bar(ax=ax[0])\n","ax[0].set_title('Survived Count',fontsize=25)\n","ax[0].tick_params(labelsize=20)\n","\n","train_df[['Sex','Survived']].groupby(['Sex']).mean().plot.bar(ax=ax[1])\n","ax[1].set_title('Survived Mean',fontsize=25)\n","ax[1].tick_params(labelsize=20)\n","\n","plt.tight_layout()\n","plt.savefig('survived_count_mean.png')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":506},"id":"O1KscVQ6mOif","executionInfo":{"status":"ok","timestamp":1695346141414,"user_tz":-540,"elapsed":1092,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"1dec5de8-0dcc-4112-a766-9400cc97a0d7"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["print(\"dead:\\n\", train_df[train_df['Survived']==0]['Sex'].value_counts())\n","print('==========')\n","print(\"survived:\\n\", train_df[train_df['Survived']==1]['Sex'].value_counts())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dJcnqZhkxpRu","executionInfo":{"status":"ok","timestamp":1695346186940,"user_tz":-540,"elapsed":269,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"35c0dd06-38b9-4ee2-fa11-ed9304faf901"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["dead:\n"," male      468\n","female     81\n","Name: Sex, dtype: int64\n","==========\n","survived:\n"," female    233\n","male      109\n","Name: Sex, dtype: int64\n"]}]},{"cell_type":"markdown","source":["Code B"],"metadata":{"id":"3u21o9sJgnoI"}},{"cell_type":"code","source":["male = train_df[train_df['Sex']=='male']['Survived'].value_counts()\n","female = train_df[train_df['Sex']=='female']['Survived'].value_counts()\n","df_survived = pd.DataFrame([male,female])\n","df_survived.index = ['male','female']\n","df_survived.plot(kind='bar',stacked=True, figsize=(8,5))\n","plt.legend(['dead', 'survived'])\n","\n","plt.tick_params(labelsize=20)\n","plt.tight_layout()\n","plt.savefig('survived_sex.png')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":507},"id":"AWDpzO4TrL9Y","executionInfo":{"status":"ok","timestamp":1695346193159,"user_tz":-540,"elapsed":340,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"aab1880e-c1e1-431a-f488-e8ca42b23bae"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### 데이터 살펴보기"],"metadata":{"id":"X41qZAoz-h86"}},{"cell_type":"code","source":["# 성별에 따른 생존율\n","train_df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"qtkgAAbs8CXg","executionInfo":{"status":"ok","timestamp":1695346600509,"user_tz":-540,"elapsed":269,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"c4b0fcca-8bb5-419c-e3d6-416eabc9d598"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Sex  Survived\n","0  female  0.742038\n","1    male  0.188908"],"text/html":["\n","  <div id=\"df-6bac1a86-fa98-42f3-bea8-3463508e75f2\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe 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\n"},"metadata":{}}]},{"cell_type":"code","source":["# SibSp(가족 수)에 따른 생존율\n","train_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=True).mean().sort_values(by='Survived', ascending=False).plot.bar()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":462},"id":"JYXbYf7R9i7w","executionInfo":{"status":"ok","timestamp":1695346786715,"user_tz":-540,"elapsed":11,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"bdb1755c-76d4-45bc-c151-d04db286bed5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='SibSp'>"]},"metadata":{},"execution_count":73},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# 생존/사망자의 나의 분포\n","g = sns.FacetGrid(train_df, hue='Survived',aspect=2)\n","g.map(sns.histplot, 'Age', bins=20)\n","g.add_legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":307},"id":"tFGxmln9DkQ4","executionInfo":{"status":"ok","timestamp":1695346975146,"user_tz":-540,"elapsed":979,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"d13815eb-b300-4074-e2dc-1d6f04eed92d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 672.111x300 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# 생존/사망자의 나의 분포\n","g = sns.FacetGrid(train_df, hue=\"Survived\",aspect=2)\n","g.map(sns.kdeplot,'Age',fill= True)\n","g.set(xlim=(0, train_df['Age'].max()))\n","g.add_legend()\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":307},"id":"ho5RZLcvDkM4","executionInfo":{"status":"ok","timestamp":1695346981724,"user_tz":-540,"elapsed":937,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"3d97d2e4-efcf-44aa-da47-7dbc7de50bf8"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 672.111x300 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### 새로운 feature('Title') 만들기\n","- 이름 중간에 포함된 Mr, Miss와 같은 title 추출\n","- 예: \"Braund, Mr. Owen Harris\""],"metadata":{"id":"njMzY08Sk9FB"}},{"cell_type":"code","source":["# dropna() --> n/a를 제외한 항목만.  명시적으로 dropna()를 하지 않아도 평균은 동일한 값으로 구해진다.\n","train_df['Age'].dropna().mean(), test_df['Age'].dropna().mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_WNleNFHnnjQ","executionInfo":{"status":"ok","timestamp":1695347455176,"user_tz":-540,"elapsed":290,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"de9fee96-dd65-4c86-8673-e0695abfb094"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(29.69911764705882, 30.272590361445783)"]},"metadata":{},"execution_count":81}]},{"cell_type":"code","source":["train_df['Title'] = train_df['Name'].str.extract(' ([A-Za-z]+)\\.')"],"metadata":{"id":"55BXmQesh9l5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pd.crosstab(train_df['Title'], train_df['Sex'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":614},"id":"KenMhjgjh9jK","executionInfo":{"status":"ok","timestamp":1695347459219,"user_tz":-540,"elapsed":300,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"96d63bc3-ab3d-4276-a867-64148a26e139"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Sex       female  male\n","Title                 \n","Capt           0     1\n","Col            0     2\n","Countess       1     0\n","Don            0     1\n","Dr             1     6\n","Jonkheer       0     1\n","Lady           1     0\n","Major          0     2\n","Master         0    40\n","Miss         182     0\n","Mlle           2     0\n","Mme            1     0\n","Mr             0   517\n","Mrs          125     0\n","Ms             1     0\n","Rev            0     6\n","Sir            0     1"],"text/html":["\n","  <div id=\"df-cbf8c09b-fbbc-4f6f-81df-7f1bee6355b4\" 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 </tr>\n","    <tr>\n","      <th>Dr</th>\n","      <td>1</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>Jonkheer</th>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>Lady</th>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Major</th>\n","      <td>0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>Master</th>\n","      <td>0</td>\n","      <td>40</td>\n","    </tr>\n","    <tr>\n","      <th>Miss</th>\n","      <td>182</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Mlle</th>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Mme</th>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Mr</th>\n","      <td>0</td>\n","      <td>517</td>\n","    </tr>\n","    <tr>\n","      <th>Mrs</th>\n","      <td>125</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Ms</th>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Rev</th>\n","      <td>0</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>Sir</th>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cbf8c09b-fbbc-4f6f-81df-7f1bee6355b4')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      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21.845638\n","3     4.574167\n","Name: Age, dtype: float64\n","========\n","Title별 중앙값: \n"," Title\n","0    30.0\n","1    35.0\n","2    21.0\n","3     3.5\n","Name: Age, dtype: float64\n"]}]},{"cell_type":"code","source":["train_df['Age'].fillna(train_df.groupby('Title')['Age'].transform('median'), inplace=True)\n","#train_df['Age'] = train_df.groupby('Title').Age.transform(lambda x: x.fillna(x.median())) # 이렇게 처리할 수도 있다.\n","\n","# Embarked 결측치 처리 후, 변환\n","train_df['Embarked'] = train_df['Embarked'].fillna('S')\n","\n","# Fare 결측치 처리: Pclass 중앙값으로 대체\n","train_df['Fare'].fillna(train_df.groupby('Pclass')['Fare'].transform('median'), inplace=True)\n","\n","# 가족 수 합치기\n","train_df['FamilySize'] = train_df['SibSp'] + train_df['Parch'] + 1\n","\n","# 불필요한 자료 제거\n","train_df.drop( ['Name','Ticket', 'SibSp', 'Parch', 'Cabin'], axis=1, inplace=True)\n","\n","# Categorical Data --> one-hot encoding\n","train_df = pd.get_dummies(train_df,columns=['Sex','Pclass','Embarked', 'Title'])\n","\n","train_df.head(10)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":383},"id":"BBIPbOFGxPIB","executionInfo":{"status":"ok","timestamp":1695347784080,"user_tz":-540,"elapsed":286,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"54d18cba-81b6-4dd4-c96d-4ce01213e826"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   PassengerId  Survived   Age     Fare  FamilySize  Sex_female  Sex_male  \\\n","0            1         0  22.0   7.2500           2           0         1   \n","1            2         1  38.0  71.2833           2           1         0   \n","2            3         1  26.0   7.9250           1           1         0   \n","3            4         1  35.0  53.1000           2           1         0   \n","4            5         0  35.0   8.0500           1           0         1   \n","5            6         0  30.0   8.4583           1           0         1   \n","6            7         0  54.0  51.8625           1           0         1   \n","7            8         0   2.0  21.0750           5           0         1   \n","8            9         1  27.0  11.1333           3           1         0   \n","9           10         1  14.0  30.0708           2           1         0   \n","\n","   Pclass_1  Pclass_2  Pclass_3  Embarked_C  Embarked_Q  Embarked_S  Title_0  \\\n","0         0         0         1           0           0           1        1   \n","1         1         0         0           1           0           0        0   \n","2         0         0         1           0           0           1        0   \n","3         1         0         0           0           0           1        0   \n","4         0         0         1           0           0           1        1   \n","5         0         0         1           0           1           0        1   \n","6         1         0         0           0           0           1        1   \n","7         0         0         1           0           0           1        0   \n","8         0         0         1           0           0           1        0   \n","9         0         1         0           1           0           0        0   \n","\n","   Title_1  Title_2  Title_3  \n","0        0        0        0  \n","1        1        0        0  \n","2        0        1        0  \n","3        1        0        0  \n","4        0        0        0  \n","5        0        0        0  \n","6        0        0        0  \n","7        0        0        1  \n","8        1        0        0  \n","9        1        0        0  "],"text/html":["\n","  <div id=\"df-758b04df-89d3-4d08-acee-eff046f42368\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        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Missing을 가장 많은 S로\n","    dataset['Embarked'] = dataset['Embarked'].fillna('S')\n","\n","    # Fare Missing을 Pclass 중앙값으로\n","    dataset[\"Fare\"].fillna(dataset.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n","\n","    # 가족 data합치기\n","    dataset[\"FamilySize\"] = dataset[\"SibSp\"] + dataset[\"Parch\"] + 1\n","\n","    # 불필요한 자료 제거\n","    dataset.drop( ['Name', 'SibSp', 'Parch', 'Ticket', 'Cabin'], axis=1, inplace=True)\n","\n","\n","#Categorical Data --> one-hot encoding\n","train_df = pd.get_dummies(train_df,columns=['Sex','Pclass','Embarked', 'Title'])  # 4개 column 각각을 변환\n","test_df = pd.get_dummies(test_df,columns=['Sex','Pclass','Embarked', 'Title'])\n","\n","train_input = train_df.drop(['PassengerId','Survived'], axis=1).values\n","train_target = train_df['Survived'].values.reshape(-1,1)\n","\n","test_input = test_df.drop(['PassengerId'], axis=1).values"],"metadata":{"id":"xWQd9AYee2cq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_df.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dnAvOOZKxFOF","executionInfo":{"status":"ok","timestamp":1695348002085,"user_tz":-540,"elapsed":303,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"33a6ca46-b046-41bc-d36e-609d3426f803"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 17 columns):\n"," #   Column       Non-Null Count  Dtype  \n","---  ------       --------------  -----  \n"," 0   PassengerId  891 non-null    int64  \n"," 1   Survived     891 non-null    int64  \n"," 2   Age          891 non-null    float64\n"," 3   Fare         891 non-null    float64\n"," 4   FamilySize   891 non-null    int64  \n"," 5   Sex_female   891 non-null    uint8  \n"," 6   Sex_male     891 non-null    uint8  \n"," 7   Pclass_1     891 non-null    uint8  \n"," 8   Pclass_2     891 non-null    uint8  \n"," 9   Pclass_3     891 non-null    uint8  \n"," 10  Embarked_C   891 non-null    uint8  \n"," 11  Embarked_Q   891 non-null    uint8  \n"," 12  Embarked_S   891 non-null    uint8  \n"," 13  Title_0      891 non-null    uint8  \n"," 14  Title_1      891 non-null    uint8  \n"," 15  Title_2      891 non-null    uint8  \n"," 16  Title_3      891 non-null    uint8  \n","dtypes: float64(2), int64(3), uint8(12)\n","memory usage: 45.4 KB\n"]}]},{"cell_type":"code","source":["test_df.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ABrmcmQZydRP","executionInfo":{"status":"ok","timestamp":1695348004076,"user_tz":-540,"elapsed":9,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"040e1d02-30ca-4dd8-dcb9-f7b253c6a499"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 418 entries, 0 to 417\n","Data columns (total 16 columns):\n"," #   Column       Non-Null Count  Dtype  \n","---  ------       --------------  -----  \n"," 0   PassengerId  418 non-null    int64  \n"," 1   Age          418 non-null    float64\n"," 2   Fare         418 non-null    float64\n"," 3   FamilySize   418 non-null    int64  \n"," 4   Sex_female   418 non-null    uint8  \n"," 5   Sex_male     418 non-null    uint8  \n"," 6   Pclass_1     418 non-null    uint8  \n"," 7   Pclass_2     418 non-null    uint8  \n"," 8   Pclass_3     418 non-null    uint8  \n"," 9   Embarked_C   418 non-null    uint8  \n"," 10  Embarked_Q   418 non-null    uint8  \n"," 11  Embarked_S   418 non-null    uint8  \n"," 12  Title_0.0    418 non-null    uint8  \n"," 13  Title_1.0    418 non-null    uint8  \n"," 14  Title_2.0    418 non-null    uint8  \n"," 15  Title_3.0    418 non-null    uint8  \n","dtypes: float64(2), int64(2), uint8(12)\n","memory usage: 18.1 KB\n"]}]},{"cell_type":"code","source":["train_df.head(10)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":383},"id":"_ZKaOMfHxFK_","executionInfo":{"status":"ok","timestamp":1695348006439,"user_tz":-540,"elapsed":285,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"3d14243d-ef61-4b88-bead-6d8ebffd04c7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   PassengerId  Survived   Age     Fare  FamilySize  Sex_female  Sex_male  \\\n","0            1         0  22.0   7.2500           2           0         1   \n","1            2         1  38.0  71.2833           2           1         0   \n","2            3         1  26.0   7.9250           1           1         0   \n","3            4         1  35.0  53.1000           2           1         0   \n","4            5         0  35.0   8.0500           1           0         1   \n","5            6         0  30.0   8.4583           1           0         1   \n","6            7         0  54.0  51.8625           1           0         1   \n","7            8         0   2.0  21.0750           5           0         1   \n","8            9         1  27.0  11.1333           3           1         0   \n","9           10         1  14.0  30.0708           2           1         0   \n","\n","   Pclass_1  Pclass_2  Pclass_3  Embarked_C  Embarked_Q  Embarked_S  Title_0  \\\n","0         0         0         1           0           0           1        1   \n","1         1         0         0           1           0           0        0   \n","2         0         0         1           0           0           1        0   \n","3         1         0         0           0           0           1        0   \n","4         0         0         1           0           0           1        1   \n","5         0         0         1           0           1           0        1   \n","6         1         0         0           0           0           1        1   \n","7         0         0         1           0           0           1        0   \n","8         0         0         1           0           0           1        0   \n","9         0         1         0           1           0           0        0   \n","\n","   Title_1  Title_2  Title_3  \n","0        0        0        0  \n","1        1        0        0  \n","2        0        1        0  \n","3        1        0        0  \n","4        0        0        0  \n","5        0        0        0  \n","6        0        0        0  \n","7        0        0        1  \n","8        1        0        0  \n","9        1        0        0  "],"text/html":["\n","  <div id=\"df-bad2ac82-486c-4be9-a577-edeb81ae0b53\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th 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<td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>38.0</td>\n","      <td>71.2833</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>26.0</td>\n","      <td>7.9250</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>53.1000</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>35.0</td>\n","      <td>8.0500</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>30.0</td>\n","      <td>8.4583</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>54.0</td>\n","      <td>51.8625</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>8</td>\n","      <td>0</td>\n","      <td>2.0</td>\n","      <td>21.0750</td>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>9</td>\n","      <td>1</td>\n","      <td>27.0</td>\n","      <td>11.1333</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>10</td>\n","      <td>1</td>\n","      <td>14.0</td>\n","      <td>30.0708</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button 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-rf\n","\n","# 런타임 다시 시작해야 함."],"metadata":{"id":"5HJmFP4c548l"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt\n","#plt.rc('font',family='NanumBarunGothic')\n","plt.rc('font',family=['NanumGothic','sans-serif']) # plt.rcParams['font.family'] = ['NanumGothic','sans-serif']\n","plt.rc('axes', unicode_minus=False)  # plt.rcParams['axes.unicode_minus'] = False"],"metadata":{"id":"WFM2QjHG54zz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.title('한글 OK?')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":450},"id":"FB9yqjaP5-p9","executionInfo":{"status":"ok","timestamp":1695354241726,"user_tz":-540,"elapsed":740,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"3d9dba97-e89f-4eb4-a685-d257a89aab8c"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## 1.5.3 Mecab, konlpy 설치"],"metadata":{"id":"fe3yiJIn8LdO"}},{"cell_type":"code","source":["!set -x\n","!curl -s https://raw.githubusercontent.com/konlpy/konlpy/master/scripts/mecab.sh | bash -x &>-\n","!pip install mecab-python3 -qq\n","!pip install konlpy -qq"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A2AzPlA88MTG","executionInfo":{"status":"ok","timestamp":1695353603978,"user_tz":-540,"elapsed":103743,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"eec08b4e-0705-4dd7-883f-e598374b6907"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m581.7/581.7 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.4/19.4 MB\u001b[0m \u001b[31m77.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m465.3/465.3 kB\u001b[0m \u001b[31m43.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}]},{"cell_type":"code","source":["from konlpy.tag import Mecab\n","mecab=Mecab()\n","mecab.morphs('학생들이 점심을 먹고 있다.') # ['학생', '들', '이', '점심', '을', '먹', '고', '있', '다', '.']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dJufYYBv83H8","executionInfo":{"status":"ok","timestamp":1695353770533,"user_tz":-540,"elapsed":272,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"2d103983-f82e-4f45-c0ff-9a2c4f88853f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['학생', '들', '이', '점심', '을', '먹', '고', '있', '다', '.']"]},"metadata":{},"execution_count":100}]},{"cell_type":"markdown","source":["## 1.5.4 Google Colaboratory - Google Drive 연결"],"metadata":{"id":"9k_byv4C9fcz"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/my_drive') # '/content/dir_name'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V53W8-zc8XBr","executionInfo":{"status":"ok","timestamp":1695353830227,"user_tz":-540,"elapsed":20890,"user":{"displayName":"Heecheol Cho","userId":"11416361730564561965"}},"outputId":"47c1944f-811b-4f0c-cc58-133588f4ca02"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/my_drive\n"]}]},{"cell_type":"markdown","source":["## 1.5.5 코드 재현성(reproducibility)을 위한 시드 고정"],"metadata":{"id":"FJ8iTpID95LD"}},{"cell_type":"markdown","source":["Tensorflow seed 고정"],"metadata":{"id":"RWHEwIID-CLc"}},{"cell_type":"code","source":["import tensorflow as tf\n","\n","seed = 100\n","tf.keras.utils.set_random_seed(seed)  # python, numpy, tensorflow seed 고정\n","tf.config.experimental.enable_op_determinism() # 속도 저하"],"metadata":{"id":"2Z1TpdNi9igs"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Pytorch seed 고정"],"metadata":{"id":"4NCNDjag-GLz"}},{"cell_type":"code","source":["import torch\n","import numpy\n","\n","def torch_reset_seed(seed=100):\n","    torch.manual_seed(seed)\n","    if torch.cuda.is_available():\n","        torch.cuda.manual_seed_all(seed)\n","        torch.backends.cudnn.deterministic = True  # 속도 저하\n","        torch.backends.cudnn.benchmark = False     # 속도 저하"],"metadata":{"id":"TYfBnvt8-FWb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["seed = 100\n","np.random.seed(seed)\n","torch_reset_seed(seed)"],"metadata":{"id":"6dRYFJKy-W4U"},"execution_count":null,"outputs":[]}]}